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Research
Cite this article: Boada J et al. 2017
Immanent conditions determine imminent
collapses: nutrient regimes define the
resilience of macroalgal communities.
Proc. R. Soc. B 284: 20162814.
http://dx.doi.org/10.1098/rspb.2016.2814
Received: 3 January 2017
Accepted: 21 February 2017
Subject Category:
Ecology
Subject Areas:
ecology, environmental science
Keywords:
alternative stable states, catastrophic shifts,
buffer capacity, tipping points, sea urchin
barrens, compensatory feeding
Author for correspondence:
Jordi Boada
e-mail: [email protected]
Electronic supplementary material is available
online at https://dx.doi.org/10.6084/m9.figshare.c.3706870.
Immanent conditions determine
imminent collapses: nutrient regimes
define the resilience of macroalgal
communities
Jordi Boada1, Rohan Arthur1,2, David Alonso1, Jordi F. Pagès3,
Albert Pessarrodona1, Silvia Oliva4, Giulia Ceccherelli4, Luigi Piazzi4,
Javier Romero5 and Teresa Alcoverro1,2
1
Centre d’Estudis Avançats de Blanes (CEAB-CSIC), Carrer d’Accés a la cala Sant Francesc 14, 17300 Blanes, Spain
Nature Conservation Foundation, 3076/5, 4th Cross, Gokulam Park, 570002 Mysore, Karnataka, India
3
School of Ocean Sciences, Bangor University, Menai Bridge, Wales LL59 5AB, UK
4
Dipartimento di Scienze della Natura e del Territorio (DIPNET), Università di Sassari, Via Piandanna 4,
Sassari, Italy
5
Departament d’Ecologia, Facultat de Biologia, Universitat de Barcelona, Avenue Diagonal 643,
08028 Barcelona, Spain
2
JB, 0000-0002-3815-625X
Predicting where state-changing thresholds lie can be inherently complex in
ecosystems characterized by nonlinear dynamics. Unpacking the mechanisms
underlying these transitions can help considerably reduce this unpredictability. We used empirical observations, field and laboratory experiments, and
mathematical models to examine how differences in nutrient regimes mediate
the capacity of macrophyte communities to sustain sea urchin grazing. In relatively nutrient-rich conditions, macrophyte systems were more resilient to
grazing, shifting to barrens beyond 1 800 g m22 (urchin biomass), more than
twice the threshold of nutrient-poor conditions. The mechanisms driving
these differences are linked to how nutrients mediate urchin foraging and
algal growth: controlled experiments showed that low-nutrient regimes trigger
compensatory feeding and reduce plant growth, mechanisms supported by
our consumer–resource model. These mechanisms act together to halve
macrophyte community resilience. Our study demonstrates that by mediating
the underlying drivers, inherent conditions can strongly influence the buffer
capacity of nonlinear systems.
1. Introduction
Identifying where critical thresholds occur in systems characterized by nonlinear
dynamics is fundamental to objectively quantifying their resilience [1–3].
Ecosystems as diverse as freshwater lakes, grasslands, coral reefs, and macroalgal
communities show hysteretic behaviour [4]; after collapse, these systems may not
recover their initial state, even when stress conditions abate. These altered states
are typically maintained by increases in the abundance of key species that
reinforce stabilizing feedback [5]. There is growing recognition of external stressors as triggers of state shifts, and an increasing interest in understanding their
underlying mechanisms [6–9]. Several critical state-changing agents have been
identified, including overfishing, pollution and abnormal nutrient loading,
population outbreaks of grazers, and infrequent disturbances like storms, fires,
temperature anomalies, and other stochastic events [2,10]. Among the best
described of these shifts occurs when herbivorous sea urchins, released from predation, quickly overtake near-shore macrophyte communities (i.e. kelp forests),
& 2017 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution
License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original
author and source are credited.
2
barren
precipitation
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macroalgal state
resilience
feeding rate
macroalgal community density
N
Figure 1. A macroalgal community grows up to a carrying capacity N following the solid blue line in the absence of herbivores. A population of herbivores consumes this macroalgae community at a rate represented by the solid green line (intermediate pressure, May 1977). Under this consumption curve, two stable states
exist; a barren state (red point) and a well-structured macroalgal state (blue point). One unstable state exists (orange point) in which situations on the left precipitate barren formation (consumption . growth) and situations on the right enhance the macroalgal community stability (consumption , growth). The distance
between the unstable point and red points represents the barren precipitation state and the distance between the unstable point and the carrying capacity N
represents the macroalgal state resilience.
reducing them to functionally depauperate barrens [11]. These
altered states are maintained by feedback that prevents recovery even when herbivores are subsequently controlled. While
in most ecosystems these drivers are relatively well understood, accurately predicting where critical transitions lie is
not trivial. How systems respond to state-changing stressors
may differ considerably, dependent on context-specific conditions. The difficulty of controlling all potential stressors
limits most studies to examining the effects of a single major
driver [6]. In reality though, a host of other apparently insignificant factors, acting together, may be critical in predisposing
ecosystems to shifts. These factors may often vary intrinsically
between locations.
The inherent conditions that determine resilience include a
range of structuring environmental and ecological regimes
(rainfall or fire regimes, natural nutrient loads, hydrodynamics,
temperature, etc.) [4,10,12]. These may interact in complex ways
with ecosystem processes, mediating stabilizing feedback and
the mechanisms that trigger system shifts. For instance, under
intense grazing pressure, Sahelian grasslands shift from perennial grasses either to annual grasses (capable of fairly rapid
recovery) or shrubs (an altered stable state). Which trajectory
the system takes is dictated by rainfall regimes, with drought
conditions predisposing hysteretic shifts to a shrub-dominated
assemblage [13]. Similarly, the resilience of many marine
systems (coral reefs, kelp forests, etc.) can be strongly mediated
by natural nutrient regimes [14,15]; post-collapse recovery is
significantly retarded when nutrients facilitate recruitment,
growth, and space-occupation of competitors [16–18]. These
conditions can vary considerably with locations, making
ecosystem trajectories intrinsically difficult to predict [16,17].
Reducing uncertainty in complex systems requires a better
handle on how context-specific underlying conditions modify
ecosystem processes. Attempts to anticipate thresholds have
focused on examining characteristics of boundary conditions
as signals of impending change [1,2,18,19]. These may manifest
as subtle changes in the variance and skew of key system
variables, self-organized patchiness, or a slowing down in
ecosystem dynamics [20–23]. These changes in system behaviour serve as powerful early-warning indicators presaging
major state shifts—either catastrophically [19] or more continuously [24]. Typically, signals have been derived from ecosystem
models or by hind-casting of systems that have already experienced shifts. Finding meaningful predictive metrics that
work in real-world situations is still elusive. These indicators
are essentially phenomenological and dependent on longterm monitoring [19]. Their advantage is that they provide
transcendent insights on system behaviour applicable across
systems. However, they are not geared to illuminating specific
mechanisms underlying transitional responses for a particular
ecosystem. Identifying these causal mechanisms may require
acknowledging that several interdependent biotic and abiotic
processes drive the functioning of the system. Understanding
these mechanisms would allow for a clearer evaluation of
ecosystem resilience, providing managers with unambiguous
directions in prioritizing ameliorative measures.
Mediterranean rocky macroalgal communities are useful
models to explore how regional conditions may mediate ecosystem transitions: they show nonlinear responses, are relatively
simple, and occur in conditions that differ considerably in
their inherent nutrient regimes [11,25,26]. Overfished, these
systems often shift to urchin-dominated barrens when their
populations cross critical thresholds [11,26,27] (herbivory rates
surpass plant growth (figure 1)). We examined if inherent
nutrient regimes mediate where thresholds occur in response
to urchin biomass [25]. Additionally, we examined potential
mechanistic pathways by which nutrients modify these
thresholds. We hypothesize that nutrient regimes can determine the relationship between growth rate and consumption
by (i) influencing herbivore consumption rates based on food
quality and/or (ii) modifying macroalgal growth based on
nutrient availability. Given these mechanisms, macroalgal communities in low-nutrient regimes will experience sudden shifts
to barrens at lower urchin biomasses than in high-nutrient
regimes. We used complementary approaches to test these
mechanisms including empirical observations and controlled
Proc. R. Soc. B 284: 20162814
macroalgal community growth and consumption rates
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2. Material and methods
(a) Study system and principal objectives
(i) Determining thresholds under different nutrient regimes
To determine if nutrient regimes influence ecosystem thresholds,
we surveyed shallow macroalgal rocky communities in two
regions within the Mediterranean Sea characterized by different
nutrient conditions (i.e. Catalan coast in Spain—high nutrients—
and Sardinia Island in Italy—low nutrients; [30]). We confirmed
these differences by measuring nutrient content in Posidonia oceanica seagrass shoots (a reliable indicator of nutrient availability
[36]) at each location. The closest seagrass meadow was selected,
usually within tens of metres of the macroalgal community. We
collected five shoots when possible and used leaf tissue for nutrient analyses. At two sites, shoots were pooled together because
the tissue was not enough for nitrogen analyses. For the rest of
the sites the data obtained was averaged for each location. Data
were analysed by comparing nitrogen content between regions
where locations represented replicates. Results confirmed that
the selected regions differed considerably in nutrients with a 26%
difference in nitrogen content between high-nutrient and lownutrient regimes (electronic supplementary material, figure S3;
p-value , 0.03). These differences match with previous studies
and patterns observable from satellite imagery (electronic supplementary material, figures S1 and S2a). In other respects, the
two regions shared similar environmental features (sea surface
temperature, exposure, light availability, salinity approximately
37.5 ppt etc.; electronic supplementary material, figure S2). To
assess the relationship between urchin biomass and algal cover,
(ii) Mechanisms underlying the thresholds
We explored mechanisms underlying nutrient-mediated
thresholds using a combination of controlled laboratory and
field experiments.
(i) Feeding response. In the laboratory, we tested if macroalgal
nutrient content affected per capita urchin herbivory rates. We
used the Mediterranean seaweed Cystoseira mediterranea as a
model forage species because it is clearly preferred by P. lividus
[31]. Sea urchins and macroalgae were collected in the same area
(41.78 N 2.88 E). Half the macroalgae was fertilized (F) in aquaria
(approx. 10 l) with running seawater for 3 days using 6 g of fertiliser (12N : 8P : 16 K) while the other non-fertilized (NF) were kept
in natural seawater aquaria. To assess food quality, we measured
leaf nitrogen (%N) from algal fronds from each treatment (F and
NF, n ¼ 5 per treatment). Fronds were powdered and analysed
for nitrogen concentration. Fertilization successfully increased
the nutrient content by approximately 9% (electronic supplementary material, figure S2; p-value , 0.01). All collected urchins
were starved for 3 days in a holding aquarium (approx. 1 000 l).
They were then transferred to six independent aquaria for testing.
Each aquarium was divided into six compartments. Five of these
accommodated a single urchin, and one was left without urchins
(n ¼ 30). We fed half the urchins with 4 g (wet weight) of
the non-fertilized algae while the rest were fed with 4 g of the fertilized algae (n ¼ 15 urchins per treatment). Compartments left
without urchins, served as procedural controls to account for
non-feeding-related algal losses. After 6 days we weighed the
remaining algae and consumption was estimated by subtracting
the final from the initial weight and dividing by total feeding
3
Proc. R. Soc. B 284: 20162814
Shallow Mediterranean rocky shores are dominated by macroalgae, constituting a very structured and diverse community
[28,29]. Although growing in oligotrophic conditions, nutrient
regimes vary considerably between regions: western continental
shores typically receive high-nutrient inputs (i.e. riverine), while
the eastern Mediterranean and islands are comparatively nutrient poor [30]. Together with the fish, Sarpa salpa, Paracentrotus
lividus is among the most important herbivores in Western
Mediterranean macroalgal communities, precipitating regime
shifts when outbreaks occur [26,31]. Predator overfishing releases
urchin populations from predation pressure, and macroalgal
systems give way to barrens under sustained herbivory [26,32].
Once created, several reinforcing feedbacks maintain this new
state. These include enhanced post-settlement survival of sea
urchins, reduced potential for algal recruitment, reductions in
the recruitment of predatory fish, and the facilitation of other
herbivores [33 – 35]. We (i) used observational studies to establish
the relationship between nutrient levels and critical transitions in
macrophyte communities, (ii) identified the mechanisms underlying these transitions to understand how contrasting nutrient
regimes influence resilience, and (iii) built a predictive model
to explain how these transitions vary with inherent conditions.
First, we examined how algal cover changed along a gradient of
urchin biomass (from macroalgal forests to barrens) in regions
with different nutrient conditions. For the second objective, we
assessed two potential mechanisms underlying nutrient-mediated
thresholds: (i) herbivores respond to nutrient impoverishment by
changing their feeding rates and/or (ii) plant growth is facilitated
by nutrients. Finally, we triangulated these results with a predictive
consumer–resource model to understand how nutrients influence
threshold conditions. This triangulated approach helped build
confidence in the overall results.
we selected four localities per region (electronic supplementary
material, figure S1) characterized by a similar assemblage of benthic
algae (photophilic structuring algae of the genus Dictyota,
Halopteris and Padina among others). Locations within each region
were separate enough (tens to hundreds of kilometres) to avoid
pseudoreplication (electronic supplementary material, figure S1).
At each locality, we sampled the substrate (approx. 3 m
depth) using 50 50 cm quadrats at different urchin densities
(60 quadrats per site when possible; n ¼ 237 for the high-nutrient
region, n ¼ 185 for the low-nutrient region). We estimated total
algal cover (capped at 100% of turf and canopy-forming species)
as a measure of community state. As urchins can sometimes hide
between algae or inside crevices, algal cover was measured after
removing urchins from quadrats when necessary (i.e. when large
urchins were present) to avoid underestimating algal cover.
To assess grazing pressure, we counted all urchins (P. lividus and
A. lixula) within each quadrat, classifying them into size-based
age classes (post-settlers, less than 1 cm test diameter, TD; juveniles, 1–3 cm TD; young adults, 3–5 cm TD and adults, more than
5 cm TD). Size classes were used to calculate urchin biomass (wet
weight, g m22) using standard volumetric conversions for these
species [37]. Only urchins larger than 3 cm were used to assess
thresholds because smaller urchins contribute negligibly to
grazing. We used total urchin biomass as a comparative metric of
grazing pressure between regions because it integrates urchin size
and accounts for herbivory. The principal species across all the
localities was P. lividus (60–80% of total biomass).
Our observations indicated a threshold in the response of
algal cover to urchin biomass. We examined this with change
point detection methods using the package strucchange [1,38]
in R [39] for each region. The algorithm assesses whether different parts of datasets require different fits to a linear regression.
To assess the significance of every potential change point, an
F-statistic (Chow test) was computed. This test is typically used
in time-series for systems exposed to disturbances, but can be
applied to detect abrupt changes in many other datasets [1].
As the change point detection requires discrete cover values
we used mean percentage cover per urchin biomass.
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laboratory and field experiments. We developed a simple
consumer–resource model incorporating these mechanisms to
explore how nutrients mediate ecosystem state changes.
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Nþ U0 N0 U0
:
N þ U0
ð2:1Þ
Urchin addition treatments integrated the effects of both nutrientmediated growth and feeding response:
nutrient-mediated growth þ feeding response
¼ 100 Nþ Uþ N0 Uþ
:
Nþ Uþ
ð2:2Þ
This allowed us to estimate the effect of compensatory feeding
4
feeding response = equation (2:2Þ equation (2:1Þ:
(iii) Modelling nutrient-mediated thresholds
We developed a simple consumer–resource model incorporating
the mechanisms of nutrient-mediated growth and compensatory
feeding to predict differences in tipping points. In the model, algal
cover was introduced as a logistic growth curve while the effects of
urchin herbivory were modelled as a Holling Type II equation. As
a consequence, the interplay between algal demographics and
urchin herbivory determined a dynamic equilibrium for algal
cover through time. In this model, nutrients influenced both algal
growth rates and urchin herbivory—the second by mediating handling time within the Holling Type II Equation (see electronic
supplementary material, appendix A).
3. Results
(a) Determining thresholds under different nutrient
regimes
There was clear evidence that the macroalgal systems we
sampled showed alternate states in both high- and low-nutrient
regions (figure 2). Sudden changes in the community state
(per cent cover) occurred when the stressor (urchin biomass)
crossed critical values (tipping point). In high-nutrient conditions, results show that this tipping point was breached at
urchin biomasses more than twice (figure 2a) that of low-nutrient
conditions (figure 2b). Threshold analyses confirmed the existence of regime shifts in both nutrient regimes; thresholds were
found at significantly different levels of the stressor (urchin
biomass; figure 2c,d) in different nutrient conditions. Macroalgal
communities in low-nutrient conditions shifted abruptly to
urchin barrens when stressor values crossed 736 g m22
sea urchin biomass (approx. 20 sea urchins m22 of 5 cm TD,
380–1 250 g m22, 95% confidence intervals; figure 2d,f). By contrast, in high-nutrient conditions canopy-forming algae were
still present at biomasses around 1 832 g m22 (approx. 40 sea
urchins m22, 1 484–2 494 g m22, 95% confidence intervals;
figure 2c,e); beyond this level however, these systems also
collapse to urchin-dominated barrens.
(b) Mechanisms underlying thresholds
Urchins showed significant foraging plasticity, adapting to
lower nutrient conditions by increasing feeding rates (compensatory feeding). In the laboratory, urchin grazing rates were 25%
higher when offered non-fertilized C. mediterranea compared
with fertilized algae (figure 3b; p , 0.01; electronic supplementary material, figure S4). In field experiments, urchins in
unfertilized plots ate considerably more than in fertilized
plots. After accounting for nutrient-mediated growth, compensatory feeding amounted to 35.5% of total consumption in the
N0Uþ plots compared to NþUþ plots (electronic supplementary
material, figure S3).
In addition, nutrients strongly influenced the growth of
algae—growing significantly more in nutrient-rich regimes as
well as in fertilized plots. After a month of herbivore exclusion,
barren sites in both regions were recolonized by a very similar
assemblage of mixed turfs and erect algae. However, while in
the high-nutrient regime, caged sites recovered 100% of their
Proc. R. Soc. B 284: 20162814
nutrient-mediated growth ¼ 100 alone, by subtracting the effects of nutrient-mediated growth
(equation (2.1)) from the total unconsumed algae (equation (2.2))
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time (6 days). No significant change in fertilized or unfertilized
algal wet weight was detected in procedural controls. We used a
linear mixed effects model to test for differences in consumption
rates between fertilized and unfertilized treatments. The full
model included the dependent variable ‘consumption rate’, the
fixed factor ‘treatment’ (two levels: fertilized and unfertilized),
and the random effect ‘aquarium’ (six levels–the six aquaria) to
account for the variance among animals kept within the same
aquarium. Each effect was dropped sequentially, and we selected
the best model using the Akaike Information Criterion [40].
(ii) Nutrient-mediated algal growth. To measure how nutrients
influenced algal growth in field conditions, we used two bare
rock areas (completely overgrazed areas, 0% algal cover), one in
each nutrient region. There, three herbivore exclusion cages of
50 50 cm (2 cm mesh size) were established. We measured
changes in total algal cover (of both erect and turf algae) inside
each plot after one month in both high- and low-nutrient
conditions. We used change in algal cover as an indirect, comparative index of growth [41,42]. We measured algal cover using the
same methods employed in our field surveys (see above). We
used one-way ANOVAs to test for differences in growth using R
[39]. Prior to the analysis, assumptions of normality and homogeneity of variances were checked both visually and statistically
(i.e. Shapiro test and Bartlett test, respectively).
(iii) Feeding response and nutrient-mediated growth (field experiment). To further validate these mechanisms, we conducted a
manipulative experiment to independently estimate the effects of
nutrient-mediated growth and feeding response in real-world conditions. The experiment was conducted in an oligotrophic location
(41.08 N 9.08 E) at approximately 8 m depth in a landscape dominated by isolated boulders during the maximum vegetative
growth period for most algal species in the study area (May to September). Twelve isolated boulders (approx. 2 m2 in area) were
randomly selected and used as independent plots (more than 5 m
apart) in a total area of about 1 000 m2. All urchins were removed
and algae scraped off with a metal brush to ensure similar initial
conditions. Treatments were randomly assigned to plots: three fertilized (nutrient-enriched, without urchins: NþU0), three fertilized
and had urchins added at a density of 10 individuals m22 (nutrient-enriched, with urchins: NþUþ), three unfertilized and had sea
urchins added at a density of 10 individuals m22 (not enriched,
with urchins N0Uþ) and finally, three left unfertilized and without
urchins (not enriched, without urchins: N0U0). Enriched conditions
were achieved by fixing 20 5 5 cm nylon mesh bags (1 mm
mesh size), containing 150 g fertilizing pellets (15N : 9P : 9 K) to a
tile. The effectiveness of fertilization was assessed from water
samples collected at randomly selected plots. Samples were taken
approximately 1 cm from the nutrient source, filtered (0.45 mm
filter size) and analysed using a continuous-flow AA3 Auto-Analyzer to determine the seawater nitrate concentration (mg l21).
Fertilization was always successful: N concentrations in fertilized
plots were about 98% higher compared with unfertilized ones
(p , 0.01). The number of urchins in Uþ treatments was periodically controlled [33]. Algal cover on the boulders was estimated
after six weeks using four random photographic quadrats
(400 cm2 in size) of the surface of each plot. Nutrient-mediated
growth in the absence of herbivory was calculated as the percentage
difference in cover between fertilized and unfertilized treatments:
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(c)
100
80
80
60
60
40
40
20
20
5
80
F statistic
60
0
(b)
0
0
1 000 2 000 3 000 4 000 5 000
(d)
0
1 000 2 000 3 000 4 000 5 000
0
1 000 2 000 3 000 4 000 5 000
sea urchin biomass (g m–2)
(f)
100
100
80
80
60
60
25
40
F statistic
20
40
15
10
20
20
5
0
0
0
1 000 2 000 3 000 4 000 5 000
sea urchin biomass (g m–2)
0
1 000 2 000 3 000 4 000 5 000
sea urchin biomass (g m–2)
Figure 2. Bubble plots of the percentage of algal cover for different sea urchin biomass for the two regions sampled: (a) Catalan coast (n ¼ 237) and (b) Sardinia
Island (n ¼ 185). Bubble size is proportional to the number of times a specific combination of urchin biomass and macroalgal cover was recorded. Dashed red lines
indicate the position of thresholds. Photos are representative of typical barren areas in each region. Change point analysis results for high- (c) and low- (d ) nutrient
regions. Black dots show the mean algal cover at different levels of the stressor (urchin biomass in grams per square metre of wet weight). Red horizontal lines
represent the mean algal cover before and after the threshold. Grey areas represent the confidence intervals (95%) around change points detected. The F statistic
(Chow test) obtained for the forward threshold is presented for both high- (e) and low-nutrient regions (f ). F statistic peaks around the area where the change point
has been detected.
(a)
(b) compensatory feeding
high-nutrient regime
low-nutrient regime
macroalgal state resilience
compensatory
feeding
macroalgal community density
0.8
0.6
0.4
0.2
0
N
F
NF
(c) nutrient-mediated growth
100
percentage cover
barren precipitation
consumption rate
1.0
nutrientmediated
growth
macroalgal community growth and consumption rates
variation in buffer capacity
80
60
40
20
0
HN
LN
Figure 3. Conceptual diagram describing the two mechanisms evaluated; the compensatory feeding and nutrient-mediated growth of the macroalgal community
and sea urchin population described in figure 1a. Under low-nutrient regimes sea urchins compensate feeding (i.e. increased consumption rates) (b) mean consumption rate on fertilized C. mediterranea F ¼ 0.53 þ 0.04 g day21 (n ¼ 15) and on non-fertilized C. mediterranea NF ¼ 0.71 þ 0.03 g day21 (n ¼ 15) in
laboratory experiments after 6 days. At the same time under low-nutrient regimes, the macroalgal community presents retarded growth rates, (c) mean percentage
cover of erect and turf algae in the high-nutrient region HN ¼ 100% þ 0% and low nutrient region LN ¼ 29.3% þ 7.42% after one month of herbivore
exclusion.
Proc. R. Soc. B 284: 20162814
1 000 2 000 3 000 4 000 5 000
40
20
0
0
macroalgal percentage cover
(e)
100
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macroalgal percentage cover
(a)
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2 000
b=1
m = 0.01
N = 10 000
a = 0.01
sea urchin biomass
1 500
1 000
B
coexistence of stationary states
500
macroalgae state
A
0.5
1.0
t, handling time
1.5
2.0
Figure 4. Under ‘rapid searching regime’, the area of the parameter space where
regime shifts are prone to occur is relative large. The range of urchin densities that
allows for coexistence of two stable stationary states increases as handling time
increases. Parameter values are given above and have been chosen to satisfy the
‘rapid searching condition’ ðaN t m=bÞ, and match the range of sea urchin
densities found in the field (see electronic supplementary material, appendix A).
A gradient of sea urchin pressure (from A to B) takes the system from a
macroalgae-dominated state (a) to a barren state (b).
algal cover, in the low-nutrient regime, only approximately
30% of the substrate was recolonized (figure 3c; p , 0.01;
electronic supplementary material, figure S4). Fertilization
experiments confirmed these results; fertilized plots (NþU0)
showed a 35% increase in algal cover compared to controls
(N0U0) (electronic supplementary material, S3).
(c) Modelling nutrient-mediated thresholds
The consumer–resource model identified clear bistability
in this system (figure 4), with macroalgae rapidly shifting to
barrens beyond a threshold. This threshold varied with nutrient
conditions, governed largely by differences in growth rates and
handling time. Additionally, as forage encounter rates are typically high compared with algal growth rates, it requires urchin
populations to reduce very close to zero before the barren is
likely to shift back to algal forests (figure 5, electronic supplementary material; appendix A). This was true for both
nutrient regimes. This matches closely with the empirical data
and, when parametrized with ecologically meaningful values,
predicts thresholds very close to those observed in the field.
4. Discussion
While nonlinear dynamics of temperate macrophyte communities has long been recognized, identifying where
nonlinearities lie has resisted prediction. Our results indicate
that nutrient regimes can strongly determine the buffer capacity
of macroalgal communities, mediating the amount of herbivory
the system can withstand. Results from field, laboratory, and
modelling approaches, all converged to give us a clear understanding of how nutrients mediate the grazing resilience of
these near-shore benthic communities. Specifically, oligotrophic
regions are much less resilient to grazing compared with nutrient-rich regions (figure 5). We documented a clear shift to
barrens at around half the sea urchin biomass in oligotrophic
systems (relative to high-nutrient areas), indicating that they
are intrinsically less able to cope with grazing. Barren states in
both nutrient regimes were remarkably stable. Our model
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0
6
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barren
highlights the existence of hysteresis indicating that it would
require an almost-complete disappearance of sea urchins
before these systems recover their macroalgal state. This is consistent with other studies [43,44]. We identified the mechanisms
underlying differences in these shifts, showing that both compensatory grazing and reduced algal growth act together in
oligotrophic systems, endowing them with roughly half the
buffer capacity of more nutrient-rich systems (figures 3 and 5).
Understanding and quantifying how context-specific conditions influence threshold dynamics will take us one step
closer towards reducing the inevitable surprise of nonlinear
ecological systems.
Temperate macrophyte communities appear particularly
prone to catastrophic shifts [11,26], with sea urchin overgrazing
(linked to population outbreaks) being the primary trigger of
these events. While it is uncertain from our work what drives
differences in urchin populations, studies have highlighted
the importance of both supply-side processes governing
recruitment and settlement [45] as well as subsequent topdown control by fish predators [26,46–48] in driving urchin
population dynamics. Temperate macroagal systems show
catastrophic state-shifting behaviour independently of local
and regional conditions. We documented sudden shifts to
urchin barrens in both nutrient regimes, linked clearly to
increases in urchin abundance. The difference was in where
thresholds lay, with relatively nutrient-rich systems more than
twice as resilient to grazing compared with nutrient-poor
systems. Given their susceptibility to discontinuities, determining these boundary conditions is all the more important to
manage temperate macrophyte communities in functionally
healthy states. This is critical given that our model shows
strong hysteretic behaviour, an indicator of the resilience
of the altered community states [11,25]. While low-nutrient conditions make macrophyte systems much more prone to barrens,
recovery from this state appears to be independent of nutrient
regimes (figure 4, electronic supplementary material; appendix
A). The model suggests that as long as algal encounter rates are
much higher than algal regrowth, recovery from barrens will
always be very protracted. While the backward process needs
to be interpreted with caution, it indicates that erect algae are
likely to recover only when urchin abundances reach close to
zero, regardless of nutrient conditions. Once barrens are created,
urchin populations generally do not collapse although individual growth rates may decrease as they switch to feeding less
nutritious encrusting algae [25,44]. In macroalgal-barren systems, urchins continue scraping the substrate, maintaining
areas free of algae. In addition, other barren-associated biota
may also play significant roles in enhancing the stability of barrens [49]. Such reinforcing feedbacks make recovery difficult,
emphasizing the need to prevent collapses from occurring.
Being able to accurately predict transitions is essential for preventive action to be effective.
Our findings contribute to understanding the role
environmental factors play in determining the resilience of
macroalgal-dominated reefs. Wernberg et al. recently described
the collapse of kelp forests following a severe heatwave [9].
Additionally, Vergés empirically demonstrated how warmingmediated increases in fish herbivory trigger system collapses
[50]. These recent examples highlight the urgent need to uncover
the mechanisms underlying the worldwide decline of temperate
macroalgal reefs [51]. Our work shows that inherent conditions
can be critical drivers of buffer capacity by influencing both plant
growth and herbivore feeding responses. Hence, very
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low-nutrient region
100
100
80
80
60
60
F2
F2
40
40
20
20
F1
0
0
1 000 2 000 3 000 4 000 5 000
biomass (g m–2)
macroalgal community
barren
F1
0
1 000 2 000 3 000 4 000 5 000
biomass (g m–2)
macroalgal community
barren
Figure 5. Buffer capacity of macroalgal community and barrens in each nutrient regime. The bubble graph shows macroalgal percentage cover related to sea urchin
biomass in gram per metre square (wet weight) in the two regions. The dashed red line represents the threshold after a tipping point F2 is reached. F1 represents
the tipping point from which the recovery of the macroalgal state is possible. Both F1 and F2 are set in the position determined by the consumer – resource
mathematical model (see electronic supplementary material, appendix A). The schematic drawing below shows the resilience of each stable state in both nutrient
regimes. The valleys represent the alternative stable states and the depth of the valley represents the resilience of that particular state.
oligotrophic systems may be particularly vulnerable to herbivore
outbreaks. Cardona et al. [52] described how, after a pulse in productivity in a low-nutrient region, the abundance of urchins
increased, resulting in dramatic reductions in macroalgal communities. In the eastern Mediterranean, with significantly
lower nutrient conditions than the western basin [30], the
spread of the herbivorous fish Siganus spp. through the Suez
Canal has led to extreme depletions of canopy-forming algae
and, where well-developed macrophyte communities were
once dominant, now bare rock prevails [29,53]. Acknowledging
this differential vulnerability may require designing contextspecific strategies for managing these systems based on measurable differences in their inherent buffer capacity.
Our work also explores the potential mechanisms that can
explain the differential resilience of these ecosystems. Under
relatively nutrient-poor conditions, macroalgae showed clearly
reduced rates of growth and urchins offset the low quality of
plants by increasing their feeding rates to meet nutritional
requirements [54]. Both compensatory feeding and nutrientmediated growth work together (figure 4) making macroalgal
systems in low-nutrient regimes shift to barrens at much
lower herbivore levels compared with communities in
high-nutrient conditions. The underlying nutrient regime
determines the degree to which macroalgal growth can
support urchin consumption before collapsing. As we demonstrate, low-nutrient regimes increased rates of consumption by
herbivores (compensatory feeding, figure 3b; electronic supplementary material, figure S5) while simultaneously reducing
the growth capacity of macroalgae (figure 3c; electronic
supplementary material, figure S5). This maximized grazing:
primary production ratio caused a faster shift to a new macroalgae-free urchin barren (figure 3). These overshoots are much
more likely to occur in the characteristic nutrient-poor conditions
of islands making them much less resilient to herbivory
compared with other nutrient-rich systems. Under extremes of
high-nutrient conditions the situation may change. In these
scenarios (relatively rare in the Mediterranean), macroalgal community composition may change to fast-growing species, making
the system vulnerable to collapse through a completely different
set of mechanisms including shading and overgrowth [55]. Based
on our current results however, we suspect that oligotrophic
systems may follow inherently different trajectories than
non-oligotrophic systems, and need to be addressed separately.
Several studies have focused on determining signals of
impending collapse in nonlinear systems [2,19,23]. They identify potentially useful proxies (critical slowing down, increasing
variance and skewness, etc.) that may herald approaching
thresholds. Identifying these signals depends on reliable longterm monitoring, and adequate demonstration that signals
correlate with hysteretic change. There have been few realworld examples where these leading indicators have been
able to predict imminent collapse in time for ameliorative action.
Essentially phenomenological, these approaches assume that
the underlying mechanisms will vary from context to context.
Our work indicates that, where it is possible to unpack these
underlying drivers, it can help substantially in identifying
where and why tipping points occur. Additionally, clarifying
the mechanisms that govern these dynamics allows determining the role inherent conditions play in mediating critical
thresholds. Taken together, it provides a way forward to
make regime-specific predictions of buffer capacity of systems
at local to regional scales. These are admittedly much more difficult to establish in more complex systems where multiple
mechanisms act in several synergistic and antagonistic ways.
Our contention is that, even in these more complex
ecosystems, inherent conditions may predispose the system to
very different dynamics, implying very different ecosystem
responses. It is critical to shift attention to a more mechanistic
understanding of the ecological processes that govern
nonlinear systems. Determining how feedbacks interact with
context-specific conditions will help considerably improve the
predictive power of resilience models, reducing the surprise
Proc. R. Soc. B 284: 20162814
0
7
rspb.royalsocietypublishing.org
macroalgae cover (%)
high-nutrient region
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Data accessibility. Data from surveys are stored in the open access
8
this research ( projects CMT2010-22273-C02-01-02 and CMT201348027-C03-R) and supported J.B. (scholarship BES-2011-043630) and
D.A. (Ramon y Cajal fellowship). The Spanish National Research
Council supported R.A.’s visitorship (CSIC-201330E062). J.F.P.
acknowledges financial support from the Welsh Government and
Higher Education Funding Council for Wales through the Sêr
Cymru National Research Network for Low Carbon, Energy and
Environment.
Acknowledgements. Authors are very grateful to N. Bahamon for his help
in obtaining satellite data, G. Roca, S. Pinna, and S. D. Ling for interesting discussions, V. Mayoral for illustration assistance, M. Bolivar,
Y. Santana, and F. Mura for field support and especially to M. Vergés
who significantly contributed in the laboratory experiments.
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